Linear Regression Model Class
Represents a linear regression model.
Definition
Namespace: Extreme.Statistics
Assembly: Extreme.Numerics (in Extreme.Numerics.dll) Version: 8.1.23
C#
Assembly: Extreme.Numerics (in Extreme.Numerics.dll) Version: 8.1.23
public class LinearRegressionModel : RegressionModel<double>
- Inheritance
- Object → Model → RegressionModel<Double> → LinearRegressionModel
- Derived
Remarks
Use the LinearRegressionModel class to analyze a linear relationship between two or more numerical variables. A multiple linear regression model tries to express one variable, called the dependent variable, as a linear combination of one or more other variables called independent variables or predictors.
Two derived classes provide convenient interfaces for specific kinds of regression.
- The SimpleRegressionModel class represents a linear regression model with one independent variable, including linearized models like exponential and logarithmic regression.
- The PolynomialRegressionModel class represents a linear regression model where the independent variables are all powers of the same variable.
Constructors
Linear | Constructs a new LinearRegressionModel. |
Linear | Constructs a new LinearRegressionModel. |
Linear | Constructs a new LinearRegressionModel. |
Linear | Constructs a new LinearRegressionModel. |
Linear | Constructs a new LinearRegressionModel. |
Linear | Constructs a fitted linear regression model. |
Properties
Adjusted |
Gets the adjusted R Squared value for the regression.
(Inherited from RegressionModel<T>) |
Anova |
Gets the AnovaTable that summarizes the results of this model.
(Inherited from RegressionModel<T>) |
Base |
Gets an index containing the keys of the columns
that are required inputs to the model.
(Inherited from Model) |
Coefficient | Gets the coefficient of variation for the regression. |
Computed |
Gets whether the model has been computed.
(Inherited from Model) Obsolete. |
Covariance |
Gets the covariance matrix of the model parameters.
(Inherited from RegressionModel<T>) |
Data |
Gets an object that contains all the data used as input to the model.
(Inherited from Model) |
Degrees |
Gets the total degrees of freedom of the data.
(Inherited from RegressionModel<T>) |
Dependent |
Gets a vector that contains the dependent variable that is to be fitted.
(Inherited from RegressionModel<T>) |
Fitted |
Gets whether the model has been computed.
(Inherited from Model) |
FStatistic |
Gets the F statistic for the regression.
(Inherited from RegressionModel<T>) |
Independent |
Gets a matrix whose columns contain the independent variables in the model.
(Inherited from RegressionModel<T>) |
Input |
Gets the schema for the features used for fitting the model.
(Inherited from Model) |
Leverage | Returns the leverage of each observation. |
Log |
Gets the log-likelihood that the model generated the data.
(Inherited from RegressionModel<T>) |
Max |
Gets or sets the maximum degree of parallelism enabled by this instance.
(Inherited from Model) |
Model |
Gets the collection of variables used in the model.
(Inherited from Model) |
NoIntercept | Gets or sets whether to include the intercept or constant term in the regression model. |
Number |
Gets the number of observations the model is based on.
(Inherited from Model) |
Parallel |
Gets or sets an object that specifies how the calculation of the model should be parallelized.
(Inherited from Model) |
Parameters |
Gets the collection of parameters associated with this model.
(Inherited from RegressionModel<T>) |
Parameter |
Gets the values of the parameters associated with this model.
(Inherited from RegressionModel<T>) |
Predicted | Gets the predicted R Squared value of the model. |
Predictions |
Gets a vector containing the model's predicted values for the dependent variable.
(Inherited from RegressionModel<T>) |
Press | Gets the predicted residual error sum of squares (PRESS) of the model. |
PValue |
Gets the probability corresponding to the F statistic for the regression.
(Inherited from RegressionModel<T>) |
Residuals |
Gets a vector containing the residuals of the model.
(Inherited from RegressionModel<T>) |
Residual |
Gets the sum of squares of the residuals of the model.
(Inherited from RegressionModel<T>) |
Ridge | Gets or sets the coefficient of the squared norm of the regression parameters for ridge regression. |
RSquared |
Gets the R Squared value for the regression.
(Inherited from RegressionModel<T>) |
Standard |
Gets the standard error of the regression.
(Inherited from RegressionModel<T>) |
Standardize |
Gets or sets whether the variables should be standardized
prior to computing the regression.
Obsolete. |
Status |
Gets the status of the model, which determines which information is available.
(Inherited from Model) |
Steps | Gets the collection of steps performed in a stepwise regression. |
Stepwise | Gets or sets an object that specifies options for performing stepwise regression. |
Supports |
Indicates whether the model supports case weights.
(Overrides Model.SupportsWeights) |
Variance | Returns the Variance Inflation Factor (VIF) for each variable in the model. |
Weights |
Gets or sets the actual weights.
(Inherited from Model) |
Methods
Compute() |
Computes the model.
(Inherited from Model) Obsolete. |
Compute( |
Computes the model.
(Inherited from Model) Obsolete. |
Contains |
Returns whether another RegressionModel<T> is nested
within this instance.
(Inherited from RegressionModel<T>) |
Equals | Determines whether the specified object is equal to the current object. (Inherited from Object) |
Finalize | Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection. (Inherited from Object) |
Fit() |
Fits the model to the data.
(Inherited from Model) |
Fit( |
Fits the model to the data.
(Inherited from Model) |
Fit |
Computes the model to the specified input
using the specified parallelization options.
(Overrides Model.FitCore(ModelInput, ParallelOptions)) |
Get |
Returns the Akaike information criterion (AIC) value for the model.
(Inherited from RegressionModel<T>) |
Get |
Returns the Bayesian information criterion (BIC) value for the model.
(Inherited from RegressionModel<T>) |
Get | Gets the Breusch-Godfrey test for serial correlation in the residuals of the regression model. |
Get | Gets the width of the 95% confidence band around the best-fit curve at the specified point. |
Get | Gets the width of the confidence band around the best-fit curve at the specified point. |
Get | Returns Cook's distance for each of the observations. |
Get | Returns the deleted residual for each observation |
Get | Returns the DFFITS value for each of the observations. |
Get | Gets the Durbin-Watson statistic for the residuals of the regression. |
Get | Returns the externally studentized residual for each observation. |
GetHashCode | Serves as the default hash function. (Inherited from Object) |
Get | Returns a test to verify that the residuals follow a normal distribution. |
Get | Returns a test to verify that the residuals follow a normal distribution. |
Get | Gets the width of the prediction band around the best-fit curve at the specified point. |
Get | Gets the width of the prediction band around the best-fit curve at the specified point. |
Get | Returns the studentized deleted residual for each observation |
Get | Returns the studentized residual for each observation. |
GetType | Gets the Type of the current instance. (Inherited from Object) |
MemberwiseClone | Creates a shallow copy of the current Object. (Inherited from Object) |
Predict( |
Predicts the value of the output corresponding to
the specified features.
(Inherited from RegressionModel<T>) |
Predict( |
Predicts the value of the output corresponding to
the specified features.
(Inherited from RegressionModel<T>) |
Predict( |
Predicts the value of the output corresponding to
the specified features.
(Inherited from RegressionModel<T>) |
Predict |
Predicts the value of the dependent variable based on
the specified values of the features.
(Inherited from RegressionModel<T>) |
Predict |
Predicts the value of the dependent variable based on
the specified values of the features.
(Inherited from RegressionModel<T>) |
Reset |
Clears all fitted model parameters.
(Inherited from Model) Obsolete. |
Reset |
Clears all fitted model parameters.
(Inherited from Model) |
Set |
Uses the specified data frame as the source for all input variables.
(Inherited from Model) |
Summarize() |
Returns a string containing a human-readable summary of the object using default options.
(Inherited from Model) |
Summarize( |
Returns a string containing a human-readable summary of the object using the specified options.
(Inherited from RegressionModel<T>) |
ToString | Returns a string that represents the current object. (Inherited from Model) |